Active Matching

  • Margarita Chli
  • Andrew J. Davison
Conference paper

DOI: 10.1007/978-3-540-88682-2_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)
Cite this paper as:
Chli M., Davison A.J. (2008) Active Matching. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg

Abstract

In the matching tasks which form an integral part of all types of tracking and geometrical vision, there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via ‘first get candidate matches, then resolve’ consensus algorithms such as RANSAC. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with much fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking. Robust, real-time matching can be achieved even in the previously unmanageable case of jerky, rapid motion necessitating weak motion modelling and large search regions.

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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Margarita Chli
    • 1
  • Andrew J. Davison
    • 1
  1. 1.Imperial College LondonLondonUK

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